code: def _neg_log_loss(y_true, y_pred):
      """
      Evaluates the negative log-loss between the observed and
      predicted responses.
  
      Parameters
      ----------
      y_true: array-like of shape (n_samples, n_targets)
          Observed responses.
      y_pred: array-like of shape (n_samples, n_targets)
          Predicted probabilies.
  
      Returns
      -------
      Scalar quantity, measuring the negative log-loss value.
      """
      return -log_loss(y_true, y_pred)
  
imports: import copy
  import numpy
  import pandas
  from sklearn.base import BaseEstimator, RegressorMixin, ClassifierMixin
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from sklearn.metrics import r2_score, roc_auc_score, log_loss
  from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
  from sklearn.preprocessing import OneHotEncoder
  from imodels.importance.block_transformers import MDIPlusDefaultTransformer, TreeTransformer, CompositeTransformer, IdentityTransformer
  from imodels.importance.ppms import PartialPredictionModelBase, GlmClassifierPPM, RidgeRegressorPPM, LogisticClassifierPPM
  from imodels.importance.mdi_plus import ForestMDIPlus, _get_default_sample_split, _validate_sample_split, _get_sample_split_data
label_desc: Use image node_id 2 for calling a global function with example usage: _neg_log_loss(y_true, y_pred) and returns: unknown